This report is constructed purely to produce figures for the VPD-COVID phase I modelling work.
d_pop <- readRDS("d_pop.rds")
figure_maker_burden_per_mod_dis(d_pop, burden_t = "deaths", scenario_pal,
year_end = 2030, dis_name = "Yellow fever")
Deaths per year up to 2030 for Yellow Fever.
figure_maker_burden_per_mod_dis(d_pop, burden_t = "deaths", scenario_pal,
year_end = 2030, dis_name = "Measles")
Deaths per year up to 2030 for Measles.
figure_maker_burden_per_mod_dis(d_pop, burden_t = "deaths", scenario_pal,
year_end = 2030, dis_name = "Meningitis A")
Deaths per year up to 2030 for Yellow Fever.
figure_maker_burden_per_mod_dis_pop(d_pop, burden_t = "deaths", scenario_pal,
year_end = 2030, dis_name = "Yellow fever")
Deaths per 100,000 per year up to 2030 for Yellow Fever.
figure_maker_burden_per_mod_dis_pop(d_pop, burden_t = "deaths", scenario_pal,
year_end = 2030, dis_name = "Measles")
Deaths per 100,000 per year up to 2030 for Measles.
figure_maker_burden_per_mod_dis_pop(d_pop, burden_t = "deaths", scenario_pal,
year_end = 2030, dis_name = "Meningitis A")
Deaths per 100,000 per year up to 2030 for Yellow Fever.
figure_maker_burden(d_pop, burden_t = "deaths", scenario_pal)
Deaths per year up to 2030 for the model averaged predictions for (A) Measles, (B) Meningitis A and (C) Yellow Fever. Dotted lines indicate each modelling group whereas the solid line represents the mean.
figure_maker_burden(d_pop, burden_t = "dalys", scenario_pal)
DALYs per year up to 2030 for the model averaged predictions for (A) Measles, (B) Meningitis A and (C) Yellow Fever. Dotted lines indicate each modelling group whereas the solid line represents the mean.
figure_maker_burden_ribbon(d_pop, burden_t = "deaths", scenario_pal)
Deaths per year up to 2030 for the model averaged predictions for (A) Measles, (B) Meningitis A and (C) Yellow Fever. Grey ribbon represents the envelope of all model predictions - this should not be interpreted as uncertainty.
figure_maker_burden_ribbon(d_pop, burden_t = "dalys", scenario_pal)
DALYs per year up to 2030 for the model averaged predictions for (A) Measles, (B) Meningitis A and (C) Yellow Fever. Grey ribbon represents the envelope of all model predictions - this should not be interpreted as uncertainty.
d_burden <-
d_pop %>%
filter(burden_outcome == "deaths") %>%
filter(year %in% c(2020, 2030)) %>%
group_by(country, year, modelling_group, simple_scenario, disease) %>%
summarise(value = sum(focal_burden, na.rm = TRUE)) %>%
bind_rows(d %>%
filter(burden_outcome == "deaths",
simple_scenario == "Postpone 2020 SIAs -> 2021",
year %in% c(2020, 2030)) %>%
group_by(country, year, disease, modelling_group) %>%
summarise(value = sum(baseline_burden, na.rm = TRUE)) %>%
mutate(simple_scenario = "BAU"))
d_burden %>%
filter(country == "NGA") %>%
arrange(year, disease) %>%
mutate(value = round(value)) %>%
pivot_wider(names_from = simple_scenario,
values_from = value) %>%
flextable::flextable(cwidth = 1.5)
|
country |
year |
modelling_group |
disease |
50% RI |
Postpone 2020 SIAs -> 2021 |
50% RI, postpone 2020 SIAs -> 2021 |
BAU |
|
NGA |
2,020 |
LSHTM-Jit |
Measles |
0 |
0 |
0 |
0 |
|
NGA |
2,020 |
McCarthy-NGA |
Measles |
5,680 |
5,853 |
5,875 |
5,344 |
|
NGA |
2,020 |
PSU-Ferrari |
Measles |
1,701 |
1,441 |
1,855 |
1,318 |
|
NGA |
2,020 |
Cambridge-Trotter |
Meningitis A |
0 |
0 |
0 |
0 |
|
NGA |
2,020 |
KPW-Jackson |
Meningitis A |
1 |
1 |
1 |
1 |
|
NGA |
2,020 |
IC-Garske |
Yellow fever |
3,035 |
3,431 |
3,496 |
2,981 |
|
NGA |
2,020 |
UND-Perkins |
Yellow fever |
18,385 |
18,029 |
18,385 |
18,029 |
|
NGA |
2,030 |
LSHTM-Jit |
Measles |
2,175 |
6 |
6,303 |
5 |
|
NGA |
2,030 |
McCarthy-NGA |
Measles |
1,336 |
1,536 |
1,476 |
1,476 |
|
NGA |
2,030 |
PSU-Ferrari |
Measles |
39 |
29 |
39 |
24 |
|
NGA |
2,030 |
Cambridge-Trotter |
Meningitis A |
0 |
0 |
0 |
0 |
|
NGA |
2,030 |
KPW-Jackson |
Meningitis A |
4 |
3 |
3 |
4 |
|
NGA |
2,030 |
IC-Garske |
Yellow fever |
2,950 |
2,869 |
2,903 |
2,915 |
|
NGA |
2,030 |
UND-Perkins |
Yellow fever |
14,413 |
13,894 |
14,051 |
14,209 |
figure_maker_burden_pop(d_pop, burden_t = "deaths", scenario_pal)
Deaths per 100,000 per year up to 2030 for the model averaged predictions for Measles, Meningitis A and Yellow Fever. Dotted lines indicate each modelling group whereas the solid line represents the mean.
figure_maker_burden_pop(d_pop, burden_t = "dalys", scenario_pal)
DALYs per 100,000 per year up to 2030 for the model averaged predictions for Measles, Meningitis A and Yellow Fever. Dotted lines indicate each modelling group whereas the solid line represents the mean.
figure_maker_burden_per_pop_ribbon(d_pop, burden_t = "deaths", scenario_pal)
Deaths per 100,000 per year up to 2030 for the model averaged predictions for Measles, Meningitis A and Yellow Fever. Grey ribbon represents the envelope of all model predictions - this should not be interpreted as uncertainty.
figure_maker_burden_per_pop_ribbon(d_pop, burden_t = "dalys", scenario_pal)
DALYs per 100,000 per year up to 2030 for the model averaged predictions for Measles, Meningitis A and Yellow Fever. Grey ribbon represents the envelope of all model predictions - this should not be interpreted as uncertainty.
figure_maker_excess_country(d_pop, "deaths", scenario_pal)
Excess deaths per year from 2020 to 2030 for the model averaged predictions for Measles, Meningitis A and Yellow Fever by country. The error bars range from max to min group preditions.
figure_maker_excess_country(d_pop, "dalys", scenario_pal)
Excess DALYs per year from 2020 to 2030 for the model averaged predictions for Measles, Meningitis A and Yellow Fever by country. The error bars range from max to min group preditions.
figure_maker_excess_country_pop(d_pop, "deaths", scenario_pal)
Excess deaths per 100,000 population per year from 2020 to 2030 for the model averaged predictions for Measles, Meningitis A and Yellow Fever by country.The error bars range from max to min group preditions.
figure_maker_excess_country_pop(d_pop, "dalys", scenario_pal)
Excess DALYs per 100,000 population per year from 2020 to 2030 for the model averaged predictions for Measles, Meningitis A and Yellow Fever by country.The error bars range from max to min group preditions.
figure_maker_norm(d_pop, "deaths", scenario_pal)
Normalised excess deaths from 2020 to 2030 for the model averaged predictions for Measles, Meningitis A and Yellow Fever.
figure_maker_norm_per_country(d_pop, "deaths", scenario_pal)
Normalised excess deaths from 2020 to 2030 for the model averaged predictions for Measles, Meningitis A and Yellow Fever per country
figure_maker_norm(d_pop, "dalys", scenario_pal)
Normalised excess DALYs from 2020 to 2030 for the model averaged predictions for Measles, Meningitis A and Yellow Fever.
figure_maker_norm_per_country(d_pop, "dalys", scenario_pal)
Normalised excess DALYs from 2020 to 2030 for the model averaged predictions for Measles, Meningitis A and Yellow Fever per country
figure_maker_norm(d_pop, "deaths", scenario_pal, year_end = 2100)
Normalised excess deaths from 2020 to 2100 for the model averaged predictions for Measles, Meningitis A and Yellow Fever.
figure_maker_norm_per_country(d_pop, "deaths", scenario_pal, year_end = 2100)
Normalised excess deaths from 2020 to 2100 for the model averaged predictions for Measles, Meningitis A and Yellow Fever per country
figure_maker_norm(d_pop, "dalys", scenario_pal, year_end = 2100)
Normalised excess DALYs from 2020 to 2100 for the model averaged predictions for Measles, Meningitis A and Yellow Fever.
figure_maker_norm_per_country(d_pop, "dalys", scenario_pal, year_end = 2100)
Normalised excess DALYs from 2020 to 2100 for the model averaged predictions for Measles, Meningitis A and Yellow Fever per country
d_pop %>%
filter(year<=2030) %>%
filter(grepl("deaths", burden_outcome)) %>%
mutate(under5 = ifelse(grepl("under5", burden_outcome),
"<5",
"All ages")) %>%
group_by(modelling_group_tidy, simple_scenario, disease, under5) %>%
summarise(Excess = -sum(impact, na.rm = TRUE)) %>%
mutate(Excess = round(Excess)) %>%
mutate(disease = factor(disease, levels = rev(c("Measles", "Meningitis A", "Yellow fever")))) %>%
mutate(mod_grp_dis = paste0(disease, ", ",modelling_group_tidy)) %>%
ggplot()+
geom_col(position = "dodge", color = "black")+
aes(x = mod_grp_dis, y = Excess, fill = under5)+
coord_flip()+
facet_wrap(simple_scenario~., nrow = 2, scales = "free_y")+
theme_minimal()+
scale_fill_manual(values = c("black", "white"))+
labs(y = "Excess deaths", x = "", fill = "Age" )